![]() Should we build or buy an AI/ML-based planogram?.Do we have historical sales data available in an accessible state to adopt for AI/ML?.The three key aspects that help to build a successful ML-based planogram assortment are: That said, artificial intelligence (AI) and machine learning (ML) have started playing a critical role in the planogram assortment by helping to rank and recommend the products to maximize sales. Retail and consumer product companies have begun to realize that the traditional approach has its limitations and needs to be reimagined in a rapidly evolving and highly competitive market. Traditionally, the retail and consumer product industries relied heavily on their past statistics for forecasting and used heuristic methods and human judgment to perform a planogram product assortment, resulting in lost sales due to out-of-stock product(s), generating greater waste due to decayed product(s), and entailing high service levels in stocking up merchandise. A blog post by Chida Sadayappan, lead specialist, cloud, data, and machine learning, Deloitte Consulting LLP and Dinesh Kumar, principal machine learning and deep learning engineer, Deloitte Consulting LLPĪ planogram is a model that specifies exactly how much product should be displayed on store shelves to maximize sales and enhance the customer experience. ![]()
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